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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationTue, 13 Dec 2011 05:00:00 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t1323770423z5ap7c03sulihye.htm/, Retrieved Thu, 02 May 2024 22:25:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154326, Retrieved Thu, 02 May 2024 22:25:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 19:35:21] [b98453cac15ba1066b407e146608df68]
- R PD    [Recursive Partitioning (Regression Trees)] [] [2011-12-13 10:00:00] [dfe1aa60d86cf4207f33712af6589424] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1418	210907	56	396	81	3	79	30	115	94	112285	24188	146283	144	145
869	120982	56	297	55	4	58	28	109	103	84786	18273	98364	103	101
1530	176508	54	559	50	12	60	38	146	93	83123	14130	86146	98	98
2172	179321	89	967	125	2	108	30	116	103	101193	32287	96933	135	132
901	123185	40	270	40	1	49	22	68	51	38361	8654	79234	61	60
463	52746	25	143	37	3	0	26	101	70	68504	9245	42551	39	38
3201	385534	92	1562	63	0	121	25	96	91	119182	33251	195663	150	144
371	33170	18	109	44	0	1	18	67	22	22807	1271	6853	5	5
1192	101645	63	371	88	0	20	11	44	38	17140	5279	21529	28	28
1583	149061	44	656	66	5	43	26	100	93	116174	27101	95757	84	84
1439	165446	33	511	57	0	69	25	93	60	57635	16373	85584	80	79
1764	237213	84	655	74	0	78	38	140	123	66198	19716	143983	130	127
1495	173326	88	465	49	7	86	44	166	148	71701	17753	75851	82	78
1373	133131	55	525	52	7	44	30	99	90	57793	9028	59238	60	60
2187	258873	60	885	88	3	104	40	139	124	80444	18653	93163	131	131
1491	180083	66	497	36	9	63	34	130	70	53855	8828	96037	84	84
4041	324799	154	1436	108	0	158	47	181	168	97668	29498	151511	140	133
1706	230964	53	612	43	4	102	30	116	115	133824	27563	136368	151	150
2152	236785	119	865	75	3	77	31	116	71	101481	18293	112642	91	91
1036	135473	41	385	32	0	82	23	88	66	99645	22530	94728	138	132
1882	202925	61	567	44	7	115	36	139	134	114789	15977	105499	150	136
1929	215147	58	639	85	0	101	36	135	117	99052	35082	121527	124	124
2242	344297	75	963	86	1	80	30	108	108	67654	16116	127766	119	118
1220	153935	33	398	56	5	50	25	89	84	65553	15849	98958	73	70
1289	132943	40	410	50	7	83	39	156	156	97500	16026	77900	110	107
2515	174724	92	966	135	0	123	34	129	120	69112	26569	85646	123	119
2147	174415	100	801	63	0	73	31	118	114	82753	24785	98579	90	89
2352	225548	112	892	81	5	81	31	118	94	85323	17569	130767	116	112
1638	223632	73	513	52	0	105	33	125	120	72654	23825	131741	113	108
1222	124817	40	469	44	0	47	25	95	81	30727	7869	53907	56	52
1812	221698	45	683	113	0	105	33	126	110	77873	14975	178812	115	112
1677	210767	60	643	39	3	94	35	135	133	117478	37791	146761	119	116
1579	170266	62	535	73	4	44	42	154	122	74007	9605	82036	129	123
1731	260561	75	625	48	1	114	43	165	158	90183	27295	163253	127	125
807	84853	31	264	33	4	38	30	113	109	61542	2746	27032	27	27
2452	294424	77	992	59	2	107	33	127	124	101494	34461	171975	175	162
829	101011	34	238	41	0	30	13	52	39	27570	8098	65990	35	32
1940	215641	46	818	69	0	71	32	121	92	55813	4787	86572	64	64
2662	325107	99	937	64	0	84	36	136	126	79215	24919	159676	96	92
186	7176	17	70	1	0	0	0	0	0	1423	603	1929	0	0
1499	167542	66	507	59	2	59	28	108	70	55461	16329	85371	84	83
865	106408	30	260	32	1	33	14	46	37	31081	12558	58391	41	41
1793	96560	76	503	129	0	42	17	54	38	22996	7784	31580	47	47
2527	265769	146	927	37	2	96	32	124	120	83122	28522	136815	126	120
2747	269651	67	1269	31	10	106	30	115	93	70106	22265	120642	105	105
1324	149112	56	537	65	6	56	35	128	95	60578	14459	69107	80	79
2702	175824	107	910	107	0	57	20	80	77	39992	14526	50495	70	65
1383	152871	58	532	74	5	59	28	97	90	79892	22240	108016	73	70
1179	111665	34	345	54	4	39	28	104	80	49810	11802	46341	57	55
2099	116408	61	918	76	1	34	39	59	31	71570	7623	78348	40	39
4308	362301	119	1635	715	2	76	34	125	110	100708	11912	79336	68	67
918	78800	42	330	57	2	20	26	82	66	33032	7935	56968	21	21
1831	183167	66	557	66	0	91	39	149	138	82875	18220	93176	127	127
3373	277965	89	1178	106	8	115	39	149	133	139077	19199	161632	154	152
1713	150629	44	740	54	3	85	33	122	113	71595	19918	87850	116	113
1438	168809	66	452	32	0	76	28	118	100	72260	21884	127969	102	99
496	24188	24	218	20	0	8	4	12	7	5950	2694	15049	7	7
2253	329267	259	764	71	8	79	39	144	140	115762	15808	155135	148	141
744	65029	17	255	21	5	21	18	67	61	32551	3597	25109	21	21
1161	101097	64	454	70	3	30	14	52	41	31701	5296	45824	35	35
2352	218946	41	866	112	1	76	29	108	96	80670	25239	102996	112	109
2144	244052	68	574	66	5	101	44	166	164	143558	29801	160604	137	133
4691	341570	168	1276	190	1	94	21	80	78	117105	18450	158051	135	123
1112	103597	43	379	66	1	27	16	60	49	23789	7132	44547	26	26
2694	233328	132	825	165	5	92	28	107	102	120733	34861	162647	230	230
1973	256462	105	798	56	0	123	35	127	124	105195	35940	174141	181	166
1769	206161	71	663	61	12	75	28	107	99	73107	16688	60622	71	68
3148	311473	112	1069	53	8	128	38	146	129	132068	24683	179566	147	147
2474	235800	94	921	127	8	105	23	84	62	149193	46230	184301	190	179
2084	177939	82	858	63	8	55	36	141	73	46821	10387	75661	64	61
1954	207176	70	711	38	8	56	32	123	114	87011	21436	96144	105	101
1226	196553	57	503	50	2	41	29	111	99	95260	30546	129847	107	108
1389	174184	53	382	52	0	72	25	98	70	55183	19746	117286	94	90
1496	143246	103	464	42	5	67	27	105	104	106671	15977	71180	116	114
2269	187559	121	717	76	8	75	36	135	116	73511	22583	109377	106	103
1833	187681	62	690	67	2	114	28	107	91	92945	17274	85298	143	142
1268	119016	52	462	50	5	118	23	85	74	78664	16469	73631	81	79
1943	182192	52	657	53	12	77	40	155	138	70054	14251	86767	89	88
893	73566	32	385	39	6	22	23	88	67	22618	3007	23824	26	25
1762	194979	62	577	50	7	66	40	155	151	74011	16851	93487	84	83
1403	167488	45	619	77	2	69	28	104	72	83737	21113	82981	113	113
1425	143756	46	479	57	0	105	34	132	120	69094	17401	73815	120	118
1857	275541	63	817	73	4	116	33	127	115	93133	23958	94552	110	110
1840	243199	75	752	34	3	88	28	108	105	95536	23567	132190	134	129
1502	182999	88	430	39	6	73	34	129	104	225920	13065	128754	54	51
1441	135649	46	451	46	2	99	30	116	108	62133	15358	66363	96	93
1420	152299	53	537	63	0	62	33	122	98	61370	14587	67808	78	76
1416	120221	37	519	35	1	53	22	85	69	43836	12770	61724	51	49
2970	346485	90	1000	106	0	118	38	147	111	106117	24021	131722	121	118
1317	145790	63	637	43	5	30	26	99	99	38692	9648	68580	38	38
1644	193339	78	465	47	2	100	35	87	71	84651	20537	106175	145	141
870	80953	25	437	31	0	49	8	28	27	56622	7905	55792	59	58
1654	122774	45	711	162	0	24	24	90	69	15986	4527	25157	27	27
1054	130585	46	299	57	5	67	29	109	107	95364	30495	76669	91	91
937	112611	41	248	36	0	46	20	78	73	26706	7117	57283	48	48
3004	286468	144	1162	263	1	57	29	111	107	89691	17719	105805	68	63
2008	241066	82	714	78	0	75	45	158	93	67267	27056	129484	58	56
2547	148446	91	905	63	1	135	37	141	129	126846	33473	72413	150	144
1885	204713	71	649	54	1	68	33	122	69	41140	9758	87831	74	73
1626	182079	63	512	63	2	124	33	124	118	102860	21115	96971	181	168
1468	140344	53	472	77	6	33	25	93	73	51715	7236	71299	65	64
2445	220516	62	905	79	1	98	32	124	119	55801	13790	77494	97	97
1964	243060	63	786	110	4	58	29	112	104	111813	32902	120336	121	117
1381	162765	32	489	56	2	68	28	108	107	120293	25131	93913	99	100
1369	182613	39	479	56	3	81	28	99	99	138599	30910	136048	152	149
1659	232138	62	617	43	0	131	31	117	90	161647	35947	181248	188	187
2888	265318	117	925	111	10	110	52	199	197	115929	29848	146123	138	127
1290	85574	34	351	71	0	37	21	78	36	24266	6943	32036	40	37
2845	310839	92	1144	62	9	130	24	91	85	162901	42705	186646	254	245
1982	225060	93	669	56	7	93	41	158	139	109825	31808	102255	87	87
1904	232317	54	707	74	0	118	33	126	106	129838	26675	168237	178	177
1391	144966	144	458	60	0	39	32	122	50	37510	8435	64219	51	49
602	43287	14	214	43	4	13	19	71	64	43750	7409	19630	49	49
1743	155754	61	599	68	4	74	20	75	31	40652	14993	76825	73	73
1559	164709	109	572	53	0	81	31	115	63	87771	36867	115338	176	177
2014	201940	38	897	87	0	109	31	119	92	85872	33835	109427	94	94
2143	235454	73	819	46	0	151	32	124	106	89275	24164	118168	120	117
2146	220801	75	720	105	1	51	18	72	63	44418	12607	84845	66	60
874	99466	50	273	32	0	28	23	91	69	192565	22609	153197	56	55
1590	92661	61	508	133	1	40	17	45	41	35232	5892	29877	39	39
1590	133328	55	506	79	0	56	20	78	56	40909	17014	63506	66	64
1210	61361	77	451	51	0	27	12	39	25	13294	5394	22445	27	26
2072	125930	75	699	207	4	37	17	68	65	32387	9178	47695	65	64
1281	100750	72	407	67	0	83	30	119	93	140867	6440	68370	58	58
1401	224549	50	465	47	4	54	31	117	114	120662	21916	146304	98	95
834	82316	32	245	34	4	27	10	39	38	21233	4011	38233	25	25
1105	102010	53	370	66	3	28	13	50	44	44332	5818	42071	26	26
1272	101523	42	316	76	0	59	22	88	87	61056	18647	50517	77	76
1944	243511	71	603	65	0	133	42	155	110	101338	20556	103950	130	129
391	22938	10	154	9	0	12	1	0	0	1168	238	5841	11	11
761	41566	35	229	42	5	0	9	36	27	13497	70	2341	2	2
1605	152474	65	577	45	0	106	32	123	83	65567	22392	84396	101	101
530	61857	25	192	25	4	23	11	32	30	25162	3913	24610	31	28
1988	99923	66	617	115	0	44	25	99	80	32334	12237	35753	36	36
1386	132487	41	411	97	0	71	36	136	98	40735	8388	55515	120	89
2395	317394	86	975	53	1	116	31	117	82	91413	22120	209056	195	193
387	21054	16	146	2	0	4	0	0	0	855	338	6622	4	4
1742	209641	42	705	52	5	62	24	88	60	97068	11727	115814	89	84
620	22648	19	184	44	0	12	13	39	28	44339	3704	11609	24	23
449	31414	19	200	22	0	18	8	25	9	14116	3988	13155	39	39
800	46698	45	274	35	0	14	13	52	33	10288	3030	18274	14	14
1684	131698	65	502	74	0	60	19	75	59	65622	13520	72875	78	78
1050	91735	35	382	103	0	7	18	71	49	16563	1421	10112	15	14
2699	244749	95	964	144	2	98	33	124	115	76643	20923	142775	106	101
1606	184510	49	537	60	7	64	40	151	140	110681	20237	68847	83	82
1502	79863	37	438	134	1	29	22	71	49	29011	3219	17659	24	24
1204	128423	64	369	89	8	32	38	145	120	92696	3769	20112	37	36
1138	97839	38	417	42	2	25	24	87	66	94785	12252	61023	77	75
568	38214	34	276	52	0	16	8	27	21	8773	1888	13983	16	16
1459	151101	32	514	98	2	48	35	131	124	83209	14497	65176	56	55
2158	272458	65	822	99	0	100	43	162	152	93815	28864	132432	132	131
1111	172494	52	389	52	0	46	43	165	139	86687	21721	112494	144	131
1421	108043	62	466	29	1	45	14	54	38	34553	4821	45109	40	39
2833	328107	65	1255	125	3	129	41	159	144	105547	33644	170875	153	144
1955	250579	83	694	106	0	130	38	147	120	103487	15923	180759	143	139
2922	351067	95	1024	95	3	136	45	170	160	213688	42935	214921	220	211
1002	158015	29	400	40	0	59	31	119	114	71220	18864	100226	79	78
1060	98866	18	397	140	0	25	13	49	39	23517	4977	32043	50	50
956	85439	33	350	43	0	32	28	104	78	56926	7785	54454	39	39
2186	229242	247	719	128	4	63	31	120	119	91721	17939	78876	95	90
3604	351619	139	1277	142	4	95	40	150	141	115168	23436	170745	169	166
1035	84207	29	356	73	11	14	30	112	101	111194	325	6940	12	12
1417	120445	118	457	72	0	36	16	59	56	51009	13539	49025	63	57
3261	324598	110	1402	128	0	113	37	136	133	135777	34538	122037	134	133
1587	131069	67	600	61	4	47	30	107	83	51513	12198	53782	69	69
1424	204271	42	480	73	0	92	35	130	116	74163	26924	127748	119	119
1701	165543	65	595	148	1	70	32	115	90	51633	12716	86839	119	119
1249	141722	94	436	64	0	19	27	107	36	75345	8172	44830	75	65
946	116048	64	230	45	0	50	20	75	50	33416	10855	77395	63	61
1926	250047	81	651	58	0	41	18	71	61	83305	11932	89324	55	49
3352	299775	95	1367	97	9	91	31	120	97	98952	14300	103300	103	101
1641	195838	67	564	50	1	111	31	116	98	102372	25515	112283	197	196
2035	173260	63	716	37	3	41	21	79	78	37238	2805	10901	16	15
2312	254488	83	747	50	10	120	39	150	117	103772	29402	120691	140	136
1369	104389	45	467	105	5	135	41	156	148	123969	16440	58106	89	89
1577	136084	30	671	69	0	27	13	51	41	27142	11221	57140	40	40
2201	199476	70	861	46	2	87	32	118	105	135400	28732	122422	125	123
961	92499	32	319	57	0	25	18	71	55	21399	5250	25899	21	21
1900	224330	83	612	52	1	131	39	144	132	130115	28608	139296	167	163
1254	135781	31	433	98	2	45	14	47	44	24874	8092	52678	32	29
1335	74408	67	434	61	4	29	7	28	21	34988	4473	23853	36	35
1597	81240	66	503	89	0	58	17	68	50	45549	1572	17306	13	13
207	14688	10	85	0	0	4	0	0	0	6023	2065	7953	5	5
1645	181633	70	564	48	2	47	30	110	73	64466	14817	89455	96	96
2429	271856	103	824	91	1	109	37	147	86	54990	16714	147866	151	151
151	7199	5	74	0	0	7	0	0	0	1644	556	4245	6	6
474	46660	20	259	7	0	12	5	15	13	6179	2089	21509	13	13
141	17547	5	69	3	0	0	1	4	4	3926	2658	7670	3	3
1639	133368	36	535	54	1	37	16	64	57	32755	10695	66675	57	56
872	95227	34	239	70	0	37	32	111	48	34777	1669	14336	23	23
1318	152601	48	438	36	2	46	24	85	46	73224	16267	53608	61	57
1018	98146	40	459	37	0	15	17	68	48	27114	7768	30059	21	14
1383	79619	43	426	123	3	42	11	40	32	20760	7252	29668	43	43
1314	59194	31	288	247	6	7	24	80	68	37636	6387	22097	20	20
1335	139942	42	498	46	0	54	22	88	87	65461	18715	96841	82	72
1403	118612	46	454	72	2	54	12	48	43	30080	7936	41907	90	87
910	72880	33	376	41	0	14	19	76	67	24094	8643	27080	25	21
616	65475	18	225	24	2	16	13	51	46	69008	7294	35885	60	56
1407	99643	55	555	45	1	33	17	67	46	54968	4570	41247	61	59
771	71965	35	252	33	1	32	15	59	56	46090	7185	28313	85	82
766	77272	59	208	27	2	21	16	61	48	27507	10058	36845	43	43
473	49289	19	130	36	1	15	24	76	44	10672	2342	16548	25	25
1376	135131	66	481	87	0	38	15	60	60	34029	8509	36134	41	38
1232	108446	60	389	90	1	22	17	68	65	46300	13275	55764	26	25
1521	89746	36	565	114	3	28	18	71	55	24760	6816	28910	38	38
572	44296	25	173	31	0	10	20	76	38	18779	1930	13339	12	12
1059	77648	47	278	45	0	31	16	62	52	21280	8086	25319	29	29
1544	181528	54	609	69	0	32	16	61	60	40662	10737	66956	49	47
1230	134019	53	422	51	0	32	18	67	54	28987	8033	47487	46	45
1206	124064	40	445	34	1	43	22	88	86	22827	7058	52785	41	40
1205	92630	40	387	60	4	27	8	30	24	18513	6782	44683	31	30
1255	121848	39	339	45	0	37	17	64	52	30594	5401	35619	41	41
613	52915	14	181	54	0	20	18	68	49	24006	6521	21920	26	25
721	81872	45	245	25	0	32	16	64	61	27913	10856	45608	23	23
1109	58981	36	384	38	7	0	23	91	61	42744	2154	7721	14	14
740	53515	28	212	52	2	5	22	88	81	12934	6117	20634	16	16
1126	60812	44	399	67	0	26	13	52	43	22574	5238	29788	25	26
728	56375	30	229	74	7	10	13	49	40	41385	4820	31931	21	21
689	65490	22	224	38	3	27	16	62	40	18653	5615	37754	32	27
592	80949	17	203	30	0	11	16	61	56	18472	4272	32505	9	9
995	76302	31	333	26	0	29	20	76	68	30976	8702	40557	35	33
1613	104011	55	384	67	6	25	22	88	79	63339	15340	94238	42	42
2048	98104	54	636	132	2	55	17	66	47	25568	8030	44197	68	68
705	67989	21	185	42	0	23	18	71	57	33747	9526	43228	32	32
301	30989	14	93	35	0	5	17	68	41	4154	1278	4103	6	6
1803	135458	81	581	118	3	43	12	48	29	19474	4236	44144	68	67
799	73504	35	248	68	0	23	7	25	3	35130	3023	32868	33	33
861	63123	43	304	43	1	34	17	68	60	39067	7196	27640	84	77
1186	61254	46	344	76	1	36	14	41	30	13310	3394	14063	46	46
1451	74914	30	407	64	0	35	23	90	79	65892	6371	28990	30	30
628	31774	23	170	48	1	0	17	66	47	4143	1574	4694	0	0
1161	81437	38	312	64	0	37	14	54	40	28579	9620	42648	36	36
1463	87186	54	507	56	0	28	15	59	48	51776	6978	64329	47	46
742	50090	20	224	71	0	16	17	60	36	21152	4911	21928	20	18
979	65745	53	340	75	0	26	21	77	42	38084	8645	25836	50	48
675	56653	45	168	39	0	38	18	68	49	27717	8987	22779	30	29
1241	158399	39	443	42	0	23	18	72	57	32928	5544	40820	30	28
676	46455	20	204	39	0	22	17	67	12	11342	3083	27530	34	34
1049	73624	24	367	93	0	30	17	64	40	19499	6909	32378	33	33
620	38395	31	210	38	0	16	16	63	43	16380	3189	10824	34	34
1081	91899	35	335	60	0	18	15	59	33	36874	6745	39613	37	33
1688	139526	151	364	71	0	28	21	84	77	48259	16724	60865	83	80
736	52164	52	178	52	0	32	16	64	43	16734	4850	19787	32	32
617	51567	30	206	27	2	21	14	56	45	28207	7025	20107	30	30
812	70551	31	279	59	0	23	15	54	47	30143	6047	36605	43	41
1051	84856	29	387	40	1	29	17	67	43	41369	7377	40961	41	41
1656	102538	57	490	79	1	50	15	58	45	45833	9078	48231	51	51
705	86678	40	238	44	0	12	15	59	50	29156	4605	39725	19	18
945	85709	44	343	65	0	21	10	40	35	35944	3238	21455	37	34
554	34662	25	232	10	0	18	6	22	7	36278	8100	23430	33	31
1597	150580	77	530	124	0	27	22	83	71	45588	9653	62991	41	39
982	99611	35	291	81	0	41	21	81	67	45097	8914	49363	54	54
222	19349	11	67	15	0	13	1	2	0	3895	786	9604	14	14
1212	99373	63	397	92	1	12	18	72	62	28394	6700	24552	25	24
1143	86230	44	467	42	0	21	17	61	54	18632	5788	31493	25	24
435	30837	19	178	10	0	8	4	15	4	2325	593	3439	8	8
532	31706	13	175	24	0	26	10	32	25	25139	4506	19555	26	26
882	89806	42	299	64	0	27	16	62	40	27975	6382	21228	20	19
608	62088	38	154	45	1	13	16	58	38	14483	5621	23177	11	11
459	40151	29	106	22	0	16	9	36	19	13127	3997	22094	14	14
578	27634	20	189	56	0	2	16	59	17	5839	520	2342	3	1
826	76990	27	194	94	0	42	17	68	67	24069	8891	38798	40	39
509	37460	20	135	19	0	5	7	21	14	3738	999	3255	5	5
717	54157	19	201	35	0	37	15	55	30	18625	7067	24261	38	37
637	49862	37	207	32	0	17	14	54	54	36341	4639	18511	32	32
857	84337	26	280	35	0	38	14	55	35	24548	5654	40798	41	38
830	64175	42	260	48	0	37	18	72	59	21792	6928	28893	46	47
652	59382	49	227	49	0	29	12	41	24	26263	1514	21425	47	47
707	119308	30	239	48	0	32	16	61	58	23686	9238	50276	37	37
954	76702	49	333	62	0	35	21	67	42	49303	8204	37643	51	51
1461	103425	67	428	96	1	17	19	76	46	25659	5926	30377	49	45
672	70344	28	230	45	0	20	16	64	61	28904	5785	27126	21	21
778	43410	19	292	63	0	7	1	3	3	2781	4	13	1	1
1141	104838	49	350	71	1	46	16	63	52	29236	5930	42097	44	42
680	62215	27	186	26	0	24	10	40	25	19546	3710	24451	26	26
1090	69304	30	326	48	6	40	19	69	40	22818	705	14335	21	21
616	53117	22	155	29	3	3	12	48	32	32689	443	5084	4	4
285	19764	12	75	19	1	10	2	8	4	5752	2416	9927	10	10
1145	86680	31	361	45	2	37	14	52	49	22197	7747	43527	43	43
733	84105	20	261	45	0	17	17	66	63	20055	5432	27184	34	34
888	77945	20	299	67	0	28	19	76	67	25272	4913	21610	32	31
849	89113	39	300	30	0	19	14	43	32	82206	2650	20484	20	19
1182	91005	29	450	36	3	29	11	39	23	32073	2370	20156	34	34
528	40248	16	183	34	1	8	4	14	7	5444	775	6012	6	6
642	64187	27	238	36	0	10	16	61	54	20154	5576	18475	12	11
947	50857	21	165	34	0	15	20	71	37	36944	1352	12645	24	24
819	56613	19	234	37	1	15	12	44	35	8019	3080	11017	16	16
757	62792	35	176	46	0	28	15	60	51	30884	10205	37623	72	72
894	72535	14	329	44	0	17	16	64	39	19540	6095	35873	27	21

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154326&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154326&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154326&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C11432
C224120

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 143 & 2 \tabularnewline
C2 & 24 & 120 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154326&T=1

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][/ROW]
[ROW][C]C1[/C][C]143[/C][C]2[/C][/ROW]
[ROW][C]C2[/C][C]24[/C][C]120[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154326&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154326&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C11432
C224120


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 11 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
Parameters (R input):
par1 = 11 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
R code (references can be found in the software module):
library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}